code: def __init__(
      self,
      voc_root,
      year="2012",
      transforms=None,
      txt_name: str = "train.txt",
  ):
      super(VOCSegmentation, self).__init__()
      assert year in [
          "2007",
          "2012",
      ], "year must be in ['2007', '2012']"
      root = os.path.join(voc_root, "VOCdevkit", f"VOC{year}")
      assert os.path.exists(root), "path '{}' does not exist.".format(
          root
      )
      image_dir = os.path.join(root, "JPEGImages")
      mask_dir = os.path.join(root, "SegmentationClass")
  
      txt_path = os.path.join(
          root, "ImageSets", "Segmentation", txt_name
      )
      assert os.path.exists(
          txt_path
      ), "file '{}' does not exist.".format(txt_path)
      with open(os.path.join(txt_path), "r") as f:
          file_names = [
              x.strip() for x in f.readlines() if len(x.strip()) > 0
          ]
  
      self.images = [
          os.path.join(image_dir, x + ".jpg") for x in file_names
      ]
      self.masks = [
          os.path.join(mask_dir, x + ".png") for x in file_names
      ]
      assert len(self.images) == len(self.masks)
      self.transforms = transforms
  
file_path: deep-learning-for-image-processing/pytorch_segmentation/deeplab_v3/my_dataset.py
imports: import os
  import torch.utils.data
  from PIL import Image
label_desc: Use image node_id 1 to create a new VOCSegmentation object from inherited base classes: data with example: obj = VOCSegmentation(voc_root, year, transforms, txt_name)